#    Copyright 2023 Haotian Liu
#
#    Licensed under the Apache License, Version 2.0 (the "License");
#    you may not use this file except in compliance with the License.
#    You may obtain a copy of the License at
#
#        http://www.apache.org/licenses/LICENSE-2.0
#
#    Unless required by applicable law or agreed to in writing, software
#    distributed under the License is distributed on an "AS IS" BASIS,
#    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#    See the License for the specific language governing permissions and
#    limitations under the License.


from typing import List, Optional, Tuple, Union

import torch
import torch.nn as nn

from transformers import AutoConfig, AutoModelForCausalLM, \
                         LlamaConfig, LlamaModel, LlamaForCausalLM

from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers.generation.utils import GenerateOutput

from ..llava_arch import LlavaMetaModel, LlavaMetaForCausalLM


class LlavaConfig(LlamaConfig):
    model_type = "llava_llama"


class LlavaLlamaModel(LlavaMetaModel, LlamaModel):
    config_class = LlavaConfig

    def __init__(self, config: LlamaConfig):
        super(LlavaLlamaModel, self).__init__(config)


class LlavaLlamaForCausalLM(LlamaForCausalLM, LlavaMetaForCausalLM):
    config_class = LlavaConfig

    def __init__(self, config):
        super(LlamaForCausalLM, self).__init__(config)
        self.model = LlavaLlamaModel(config)
        self.pretraining_tp = config.pretraining_tp
        self.vocab_size = config.vocab_size
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        # Initialize weights and apply final processing
        self.post_init()

    def get_model(self):
        return self.model

    def forward( 
        self, 
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        images: Optional[torch.FloatTensor] = None,
        input_features: Optional[torch.FloatTensor] = None,
        features_mask: Optional[torch.Tensor] = None,
        image_mask: Optional[torch.Tensor] = None,
        return_dict: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
        audio_attention_mask: Optional[torch.Tensor] = None
    ) -> Union[Tuple, CausalLMOutputWithPast]:
        
        if inputs_embeds is None:
            (
                input_ids,
                position_ids,
                attention_mask,
                past_key_values,
                inputs_embeds,
                labels,
                _,
            ) = self.prepare_inputs_labels_for_audio_and_vision(
                input_ids, 
                position_ids,
                attention_mask, 
                past_key_values, 
                labels, 
                images, 
                image_mask, 
                input_features, 
                features_mask,
                audio_attention_mask
                )


 
        return super().forward(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            labels=labels,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            
        )




    @torch.no_grad()
    def generate(
        self,
        inputs: Optional[torch.Tensor] = None,
        images: Optional[torch.Tensor] = None,
        image_mask: Optional[torch.Tensor] = None,
        input_features: Optional[torch.Tensor] = None,
        features_mask: Optional[torch.Tensor] = None,
        audio_attention_mask: Optional[torch.Tensor] = None,
        **kwargs,
    ) -> Union[GenerateOutput, torch.LongTensor]:
        position_ids = kwargs.pop("position_ids", None)  
        attention_mask = kwargs.pop("attention_mask", None)  
        if "inputs_embeds" in kwargs:
            raise NotImplementedError("`inputs_embeds` is not supported")
        
        if images is not None or input_features is not None:
            (  
                inputs,
                position_ids,
                attention_mask,
                _,
                inputs_embeds,
                _,
                _,
            ) = self.prepare_inputs_labels_for_audio_and_vision(
                inputs,
                position_ids,
                attention_mask,
                None,
                None,
                images,
                image_mask,
                input_features,
                features_mask,
                audio_attention_mask
            )

        else:
            inputs_embeds = self.get_model().embed_tokens(inputs)

        return super().generate(
            position_ids=position_ids,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            inputs = inputs,
            **kwargs
        )



    def prepare_inputs_for_generation(self, input_ids, past_key_values=None,
                                      inputs_embeds=None, **kwargs):
        images = kwargs.pop("images", None)
        image_mask = kwargs.pop("image_mask", None)
        input_features = kwargs.pop("input_features",None)
        features_mask = kwargs.pop("features_mask",None)
        
        inputs = super().prepare_inputs_for_generation(
            input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, **kwargs
        )
        
        inputs['position_ids'] = inputs['cache_position'].unsqueeze(0)
        if images is not None:
            inputs['images'] = images
        if image_mask is not None:
            inputs['image_mask'] = image_mask
        if input_features is not None:
            inputs['input_features'] = input_features
        if features_mask is not None:
            inputs['features_mask'] = features_mask
            
        #position_ids = inputs.pop("position_ids", None)
        
        return inputs



AutoConfig.register("llava_llama", LlavaConfig)
AutoModelForCausalLM.register(LlavaConfig, LlavaLlamaForCausalLM)

